Cited by Application Domains of Aspect and Sentiment Classification Techniques: A Survey

Sentiment analysis methods, applications, and challenges: A systematic literature review DOI

Yanying Mao, Qun Liu, Yu Zhang

et al.

Journal of King Saud University - Computer and Information Sciences, Journal Year: 2024, Volume and Issue: 36(4), P. 102048 - 102048

Published: April 1, 2024

Language: Английский

Citations

Graph convolutional network based on self-attention variational autoencoder and capsule contrastive learning for aspect-based sentiment analysis DOI

Xinyue Wang, Long Liu, Zhuo Chen

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127172 - 127172

Published: March 1, 2025

Language: Английский

Citations

Semantic enhancement and cross-modal interaction fusion for sentiment analysis in social media DOI

Guangyu Mu,

Ying Chen,

Xiurong Li

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(4), P. e0321011 - e0321011

Published: April 28, 2025

The rapid development of social media has significantly impacted sentiment analysis, essential for understanding public opinion and predicting trends. However, modality fusion in analysis can introduce a lot noise because the differences semantic representations among various modalities, ultimately impacting accuracy classification results. Thus, this paper presents Semantic Enhancement Cross-Modal Interaction Fusion (SECIF) model to address these issues. Firstly, BERT ResNet extract feature from text images. Secondly, GMHA mechanism is proposed aggregate important information mitigate influence noise. Then, an ICN module created capture complex contextual dependencies enhance capability representations. Finally, cross-modal interaction implemented. Text features are considered primary, image auxiliary, enabling profound integration textual visual features. model's performance optimized by combining cross-entropy KL divergence losses. experiments conducted using dataset collected events on Sina Weibo. results demonstrate that outperforms comparison models. SECIF improves 11.19%, 82.27%, 4.83% over average text-only, image-only, multimodal models, respectively. compared with ten baseline models publicly available datasets. experimental show 4.70% F1 score 6.56%. Through governments better understand emotions trends, facilitating more targeted effective management strategies.

Language: Английский

Citations

Dual-channel relative position guided attention networks for aspect-based sentiment analysis DOI

Xuejian Gao, Liu Fang-ai, Xuqiang Zhuang

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 253, P. 124271 - 124271

Published: May 18, 2024

Language: Английский

Citations

Parameter-efficient online knowledge distillation for pretrained language models DOI

Yukun Wang, Jin Wang, Xuejie Zhang

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 265, P. 126040 - 126040

Published: Dec. 9, 2024

Language: Английский

Citations

Assessing a BERT-based model for analyzing subjectivity and classifying academic articles DOI

Atif Mehmood, Farah Shahid, Rizwan Khan

et al.

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: June 17, 2024

Language: Английский

Citations

FITE-GAT: Enhancing aspect-level sentiment classification with FT-RoBERTa induced trees and graph attention network DOI

Mengmeng Fan, Mingming Kong, Xi Wang

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: unknown, P. 125890 - 125890

Published: Nov. 1, 2024

Language: Английский

Citations

Application Domains of Aspect and Sentiment Classification Techniques: A Survey DOI

Jibran Mir, Azhar Mahmood, Shaheen Khatoon

et al.

Neurocomputing, Journal Year: 2024, Volume and Issue: unknown, P. 129237 - 129237

Published: Dec. 1, 2024

Language: Английский

Citations